You're reading from XGBoost for Regression Predictive Modeling and Time Series Analysis Learn how to build, evaluate, and deploy predictive models with expert guidance

Product type Paperback

Published in Dec 2024

Publisher Packt

ISBN-13 9781805123057

Length 308 pages

Edition 1st Edition

Concepts

Data Governance

Authors (2):

Joyce Weiner

Partha Pritam Deka

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Table of Contents (19) Chapters

Preface

1. Part 1:Introduction to Machine Learning and XGBoost with Case Studies

2. Chapter 1: An Overview of Machine Learning, Classification, and Regression FREE CHAPTER

3. Chapter 2: XGBoost Quick Start Guide with an Iris Data Case Study

4. Chapter 3: Demystifying the XGBoost Paper

5. Chapter 4: Adding on to the Quick Start – Switching out the Dataset with a Housing Data Case Study

6. Part 2: Practical Applications – Data, Features, and Hyperparameters

7. Chapter 5: Classification and Regression Trees, Ensembles, and Deep Learning Models – What’s Best for Your Data?

8. Chapter 6: Data Cleaning, Imbalanced Data, and Other Data Problems

9. Chapter 7: Feature Engineering

10. Chapter 8: Encoding Techniques for Categorical Features

11. Chapter 9: Using XGBoost for Time Series Forecasting

12. Chapter 10: Model Interpretability, Explainability, and Feature Importance with XGBoost

13. Part 3: Model Evaluation Metrics and Putting Your Model into Production

14. Chapter 11: Metrics for Model Evaluations and Comparisons

15. Chapter 12: Managing a Feature Engineering Pipeline in Training and Inference

16. Chapter 13: Deploying Your XGBoost Model

17. Index

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Training a model using multithreaded and distributed computing with XGBoost

Let’s start with a critical aspect of deploying machine learning models: training time. As you saw in Chapter 5, model training can be a time-consuming process, especially when working with large datasets or when frequent retraining is necessary.

XGBoost offers built-in support for multithreaded computing, which can significantly speed up the training process by utilizing multiple CPU cores. In this section, you’ll explore how to enable multithreading for XGBoost training, which works on both Windows and Linux systems. Then, you’ll delve into distributed computing options using Dask (www.dask.org) on Linux so that you can scale model training across clusters or in cloud environments. Let’s begin.

Using XGBoost’s multithreaded features

XGBoost has built-in support for multithreaded computing, which allows you to speed up model training by utilizing multiple CPU cores...

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You're reading from XGBoost for Regression Predictive Modeling and Time Series Analysis Learn how to build, evaluate, and deploy predictive models with expert guidance

Table of Contents (19) Chapters

Training a model using multithreaded and distributed computing with XGBoost

Using XGBoost’s multithreaded features

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Authors (2)

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